Nec corporation (20240127088). MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA simplified abstract
Contents
- 1 MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA
Organization Name
Inventor(s)
Ammar Shaker of Heidelberg (DE)
Francesco Alesiani of Heidelberg (DE)
MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127088 titled 'MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA
Simplified Explanation
The abstract of the patent application describes a method for lifelong machine learning using boosting, which involves learning a distribution of weights over a learning sample for a new task by utilizing previously learned classifiers from old tasks and learning task-specific classifiers for the new task using a boosting algorithm.
- Receiving a new task and a learning sample for the new task
- Learning a distribution of weights over the learning sample using previously learned classifiers from old tasks
- Learning task-specific classifiers for the new task using a boosting algorithm and the distribution of weights over the learning sample
- Updating the distribution of weights over the learning sample using the task-specific classifiers for the new task
Potential Applications
This technology could be applied in various fields such as:
- Autonomous vehicles
- Healthcare diagnostics
- Financial forecasting
Problems Solved
This technology addresses the following issues:
- Adapting to new tasks without forgetting previous knowledge
- Improving the efficiency of machine learning models
- Enhancing the accuracy of predictions over time
Benefits
The benefits of this technology include:
- Continuous learning and adaptation to new tasks
- Increased performance and accuracy of machine learning models
- Reduction in training time and computational resources
Potential Commercial Applications
The potential commercial applications of this technology could be seen in:
- Software development companies
- Data analytics firms
- E-commerce platforms
Possible Prior Art
One possible prior art related to this technology is the concept of ensemble learning, where multiple models are combined to improve prediction accuracy.
What are the limitations of this method in handling extremely large datasets?
This method may face challenges in processing and analyzing extremely large datasets efficiently due to computational constraints and memory limitations.
How does this method compare to traditional machine learning approaches in terms of adaptability to new tasks?
This method outperforms traditional machine learning approaches in adaptability to new tasks by leveraging previously learned knowledge and updating the learning process with task-specific classifiers.
Original Abstract Submitted
a method for lifelong machine learning using boosting includes receiving a new task and a learning sample for the new task. a distribution of weights is learned over the learning sample using previously learned classifiers from old tasks. a set of task-specific classifiers are learned for the new task using a boosting algorithm and the distribution of weights over the learning sample, whereby the distribution of weights over the learning sample is updated using the task-specific classifiers for the new task.
